In your final repo, there should be an R markdown file that organizes all computational steps for evaluating your proposed Facial Expression Recognition framework.
This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure.
if(!require("EBImage")){
install.packages("BiocManager")
BiocManager::install("EBImage")
}
if(!require("R.matlab")){
install.packages("R.matlab")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("dplyr")){
install.packages("dplyr")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("ggplot2")){
install.packages("ggplot2")
}
if(!require("caret")){
install.packages("caret")
}
if(!require("glmnet")){
install.packages("glmnet")
}
if(!require("WeightedROC")){
install.packages("WeightedROC")
}
if(!require("gbm")){
install.packages("gbm")
}
if(!require("xgboost")){
install.packages("xgboost")
}
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if(!require("caret")){
install.packages("caret")
}
# Install Miniconda (https://docs.conda.io/en/latest/miniconda.html)
if(!require("keras")){
install.packages("keras")
}
if(!require("tensorflow")){
install.packages("tensorflow")
install_tensorflow()
}
use_condaenv("r-tensorflow")
library(keras)
library(tensorflow)
library(R.matlab)
library(readxl)
library(dplyr)
library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
require(xgboost)
library(caret)
set.seed(2020)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located.
# use relative path for reproducibility
Provide directories for training images. Training images and Training fiducial points will be in different subfolders.
train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir, "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")
In this chunk, we have a set of controls for the evaluation experiments.
# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5 # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set
sample.reweight <- TRUE # run sample reweighting in model training
K <- 5 # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set
run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set
run.cv_dnn <- TRUE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set
Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.
# GBM parameters
n.trees <- c(500, 1000, 1500)
shrinkage <- c(0.01, 0.05, 0.1)
# DNN parameters
lrs <- c(0.0002, 0.0005, 0.001)
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)
If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.
n_files <- length(list.files(train_image_dir))
image_list <- list()
for(i in 1:100){
image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}
Fiducial points are stored in matlab format. In this step, we read them and store them in a list.
#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}
#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
strings not representable in native encoding will be translated to UTF-8
save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")
The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.
Figure1
feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.
feature.Rsource("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
save(dat_train, file="../output/feature_train.RData")
}else{
load(file="../output/feature_train.RData")
}
tm_feature_test <- NA
if(run.feature.test){
tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
save(dat_test, file="../output/feature_test.RData")
}else{
load(file="../output/feature_test.RData")
}
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_gbm.R")
source("../lib/test_gbm.R")
source("../lib/cross_validation_gbm.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
if(run.cv_gbm){
res_cv_gbm <- matrix(0, nrow = length(n.trees) * length(shrinkage), ncol = 6)
count = 0
for(i in 1:length(n.trees)){
for(j in 1:length(shrinkage)){
count = count + 1
cat("n.trees =", n.trees[i], "\n")
cat("shrinkage =", shrinkage[j], "\n")
res_cv <- cv.function_gbm(features = feature_train, labels = label_train, K,
n.trees[i], shrinkage[j], reweight = sample.reweight)
res_cv_gbm[count,] <- c(n.trees[i], shrinkage[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
}
}
colnames(res_cv_gbm) <- c("n.trees","shrinkage","mean_error", "sd_error", "mean_AUC", "sd_AUC")
save(res_cv_gbm, file="../output/res_cv_gbm.RData")
}else{
load("../output/res_cv_gbm.RData")
}
Visualize cross-validation results.
res_cv_gbm <- as.data.frame(res_cv_gbm)
if(run.cv_gbm){
p1 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_error,
ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_AUC,
ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p1)
print(p2)
}
# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])
par_n.trees_best <- 500
par_shrinkage_best <- 0.05
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train, file="../output/fit_train_gbm.RData")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
load(file="../output/fit_train_gbm.RData")
tm_test <- system.time({prob_pred <- test_gbm(fit_train, feature_test, par_n.trees_best, pred.type = 'response');})
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", accu*100, "%.\n")
The accuracy of model: GBM with n.trees = 500 and shrinkage = 0.05 is 74.02552 %.
cat("The AUC of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", auc, ".\n")
The AUC of model: GBM with n.trees = 500 and shrinkage = 0.05 is 0.7922946 .
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
Time for constructing training features= 0.89 s
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
Time for constructing testing features= 0.13 s
cat("Time for training model=", tm_train[3], "s \n")
Time for training model= 75.21 s
cat("Time for testing model=", tm_test[3], "s \n")
Time for testing model= 0.12 s
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
source("../lib/cross_validation_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
if(run.cv_dnn){
# res_cv_dnn <- matrix(0, 1, ncol = 4)
res_cv_dnn <- matrix(0, nrow = length(lrs), ncol = 6)
count = 0
for(i in 1:length(lrs)){
count = count + 1
cat("lr =", lrs[i], "\n")
res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
reweight = sample.reweight, lr = lrs[i])
res_cv_dnn[count,] <- c(lr[i], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
}
res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
reweight = sample.reweight, lr = lrs[i])
colnames(res_cv_dnn) <- c("lr", "mean_error", "sd_error", "mean_AUC", "sd_AUC")
save(res_cv_dnn, file="../output/res_cv_dnn.RData")
}else{
load("../output/res_cv_dnn.RData")
}
lr = 2e-04
Epoch 1/100
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39/39 [==============================] - 1s 32ms/step - loss: 1.9690 - accuracy: 0.6891
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Epoch 2/100
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39/39 [==============================] - 1s 32ms/step - loss: 1.8065 - accuracy: 0.6510
39/39 [==============================] - 1s 38ms/step - loss: 1.8065 - accuracy: 0.6510 - val_loss: 1.7415 - val_accuracy: 0.6625
Epoch 3/100
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39/39 [==============================] - 1s 38ms/step - loss: 1.7425 - accuracy: 0.6490 - val_loss: 1.6887 - val_accuracy: 0.5667
Epoch 4/100
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Epoch 5/100
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Epoch 6/100
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Epoch 7/100
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39/39 [==============================] - 1s 32ms/step - loss: 1.6204 - accuracy: 0.6714
39/39 [==============================] - 2s 40ms/step - loss: 1.6204 - accuracy: 0.6714 - val_loss: 1.6317 - val_accuracy: 0.5583
Epoch 8/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.6150 - accuracy: 0.6734
39/39 [==============================] - 2s 43ms/step - loss: 1.6150 - accuracy: 0.6734 - val_loss: 1.5954 - val_accuracy: 0.6042
Epoch 9/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.5900 - accuracy: 0.6990
39/39 [==============================] - 2s 42ms/step - loss: 1.5900 - accuracy: 0.6990 - val_loss: 1.6163 - val_accuracy: 0.5646
Epoch 10/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.5618 - accuracy: 0.6948
39/39 [==============================] - 2s 42ms/step - loss: 1.5618 - accuracy: 0.6948 - val_loss: 1.5994 - val_accuracy: 0.5708
Epoch 11/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.5327 - accuracy: 0.7156
39/39 [==============================] - 2s 42ms/step - loss: 1.5327 - accuracy: 0.7156 - val_loss: 1.5647 - val_accuracy: 0.6604
Epoch 12/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.5380 - accuracy: 0.7130
39/39 [==============================] - 2s 42ms/step - loss: 1.5380 - accuracy: 0.7130 - val_loss: 1.5329 - val_accuracy: 0.6687
Epoch 13/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.5117 - accuracy: 0.7286
39/39 [==============================] - 2s 44ms/step - loss: 1.5117 - accuracy: 0.7286 - val_loss: 1.5546 - val_accuracy: 0.6396
Epoch 14/100
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39/39 [==============================] - 1s 34ms/step - loss: 1.5016 - accuracy: 0.7203
39/39 [==============================] - 2s 41ms/step - loss: 1.5016 - accuracy: 0.7203 - val_loss: 1.5300 - val_accuracy: 0.6542
Epoch 15/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.4969 - accuracy: 0.7385
39/39 [==============================] - 2s 42ms/step - loss: 1.4969 - accuracy: 0.7385 - val_loss: 1.5181 - val_accuracy: 0.6562
Epoch 16/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.4841 - accuracy: 0.7250
39/39 [==============================] - 2s 42ms/step - loss: 1.4841 - accuracy: 0.7250 - val_loss: 1.5504 - val_accuracy: 0.6458
Epoch 17/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.4632 - accuracy: 0.7323
39/39 [==============================] - 2s 43ms/step - loss: 1.4632 - accuracy: 0.7323 - val_loss: 1.5230 - val_accuracy: 0.6771
Epoch 18/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.4656 - accuracy: 0.7370
39/39 [==============================] - 2s 43ms/step - loss: 1.4656 - accuracy: 0.7370 - val_loss: 1.5146 - val_accuracy: 0.6687
Epoch 19/100
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39/39 [==============================] - 1s 34ms/step - loss: 1.4239 - accuracy: 0.7615
39/39 [==============================] - 2s 41ms/step - loss: 1.4239 - accuracy: 0.7615 - val_loss: 1.5069 - val_accuracy: 0.6896
Epoch 20/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.4257 - accuracy: 0.7573
39/39 [==============================] - 2s 42ms/step - loss: 1.4257 - accuracy: 0.7573 - val_loss: 1.4749 - val_accuracy: 0.6958
Epoch 21/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3930 - accuracy: 0.7703
39/39 [==============================] - 2s 42ms/step - loss: 1.3930 - accuracy: 0.7703 - val_loss: 1.5150 - val_accuracy: 0.6750
Epoch 22/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.3996 - accuracy: 0.7698
39/39 [==============================] - 2s 42ms/step - loss: 1.3996 - accuracy: 0.7698 - val_loss: 1.4835 - val_accuracy: 0.7063
Epoch 23/100
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39/39 [==============================] - 1s 34ms/step - loss: 1.3859 - accuracy: 0.7760
39/39 [==============================] - 2s 42ms/step - loss: 1.3859 - accuracy: 0.7760 - val_loss: 1.4610 - val_accuracy: 0.7125
Epoch 24/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.3823 - accuracy: 0.7703
39/39 [==============================] - 2s 44ms/step - loss: 1.3823 - accuracy: 0.7703 - val_loss: 1.5027 - val_accuracy: 0.6854
Epoch 25/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3701 - accuracy: 0.7609
39/39 [==============================] - 2s 44ms/step - loss: 1.3701 - accuracy: 0.7609 - val_loss: 1.4771 - val_accuracy: 0.7042
Epoch 26/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3673 - accuracy: 0.7797
39/39 [==============================] - 2s 44ms/step - loss: 1.3673 - accuracy: 0.7797 - val_loss: 1.4553 - val_accuracy: 0.7000
Epoch 27/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.3447 - accuracy: 0.7901
39/39 [==============================] - 2s 44ms/step - loss: 1.3447 - accuracy: 0.7901 - val_loss: 1.4393 - val_accuracy: 0.7479
Epoch 28/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3390 - accuracy: 0.7839
39/39 [==============================] - 2s 44ms/step - loss: 1.3390 - accuracy: 0.7839 - val_loss: 1.4313 - val_accuracy: 0.7354
Epoch 29/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3076 - accuracy: 0.8016
39/39 [==============================] - 2s 44ms/step - loss: 1.3076 - accuracy: 0.8016 - val_loss: 1.4467 - val_accuracy: 0.7104
Epoch 30/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3095 - accuracy: 0.8057
39/39 [==============================] - 2s 43ms/step - loss: 1.3095 - accuracy: 0.8057 - val_loss: 1.4289 - val_accuracy: 0.7375
Epoch 31/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2736 - accuracy: 0.8062
39/39 [==============================] - 2s 43ms/step - loss: 1.2736 - accuracy: 0.8062 - val_loss: 1.4412 - val_accuracy: 0.7625
Epoch 32/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2742 - accuracy: 0.8167
39/39 [==============================] - 2s 44ms/step - loss: 1.2742 - accuracy: 0.8167 - val_loss: 1.4393 - val_accuracy: 0.7500
Epoch 33/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.3000 - accuracy: 0.8057
39/39 [==============================] - 2s 43ms/step - loss: 1.3000 - accuracy: 0.8057 - val_loss: 1.4924 - val_accuracy: 0.7167
Epoch 34/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.2763 - accuracy: 0.8031
39/39 [==============================] - 2s 44ms/step - loss: 1.2763 - accuracy: 0.8031 - val_loss: 1.4614 - val_accuracy: 0.7333
Epoch 35/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2541 - accuracy: 0.8172
39/39 [==============================] - 2s 44ms/step - loss: 1.2541 - accuracy: 0.8172 - val_loss: 1.4424 - val_accuracy: 0.7729
Epoch 36/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2335 - accuracy: 0.8266
39/39 [==============================] - 2s 44ms/step - loss: 1.2335 - accuracy: 0.8266 - val_loss: 1.4674 - val_accuracy: 0.7396
Epoch 37/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2388 - accuracy: 0.8120
39/39 [==============================] - 2s 44ms/step - loss: 1.2388 - accuracy: 0.8120 - val_loss: 1.4244 - val_accuracy: 0.7375
Epoch 38/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2134 - accuracy: 0.8219
39/39 [==============================] - 2s 44ms/step - loss: 1.2134 - accuracy: 0.8219 - val_loss: 1.4763 - val_accuracy: 0.7812
Epoch 39/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2147 - accuracy: 0.8255
39/39 [==============================] - 2s 44ms/step - loss: 1.2147 - accuracy: 0.8255 - val_loss: 1.4473 - val_accuracy: 0.7208
Epoch 40/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.1994 - accuracy: 0.8234
39/39 [==============================] - 2s 45ms/step - loss: 1.1994 - accuracy: 0.8234 - val_loss: 1.4255 - val_accuracy: 0.7667
Epoch 41/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.2046 - accuracy: 0.8146
39/39 [==============================] - 2s 44ms/step - loss: 1.2046 - accuracy: 0.8146 - val_loss: 1.4846 - val_accuracy: 0.7896
Epoch 42/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.1887 - accuracy: 0.8349
39/39 [==============================] - 2s 44ms/step - loss: 1.1887 - accuracy: 0.8349 - val_loss: 1.4326 - val_accuracy: 0.7437
Epoch 43/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.1457 - accuracy: 0.8490
39/39 [==============================] - 2s 44ms/step - loss: 1.1457 - accuracy: 0.8490 - val_loss: 1.4377 - val_accuracy: 0.7896
Epoch 44/100
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39/39 [==============================] - 1s 38ms/step - loss: 1.1503 - accuracy: 0.8422
39/39 [==============================] - 2s 45ms/step - loss: 1.1503 - accuracy: 0.8422 - val_loss: 1.4572 - val_accuracy: 0.7812
Epoch 45/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.1225 - accuracy: 0.8562
39/39 [==============================] - 2s 44ms/step - loss: 1.1225 - accuracy: 0.8562 - val_loss: 1.4362 - val_accuracy: 0.7625
Epoch 46/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.1106 - accuracy: 0.8536
39/39 [==============================] - 2s 44ms/step - loss: 1.1106 - accuracy: 0.8536 - val_loss: 1.4354 - val_accuracy: 0.7875
Epoch 47/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.1343 - accuracy: 0.8464
39/39 [==============================] - 2s 49ms/step - loss: 1.1343 - accuracy: 0.8464 - val_loss: 1.3956 - val_accuracy: 0.7708
Epoch 48/100
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39/39 [==============================] - 1s 35ms/step - loss: 1.1226 - accuracy: 0.8510
39/39 [==============================] - 2s 44ms/step - loss: 1.1226 - accuracy: 0.8510 - val_loss: 1.4011 - val_accuracy: 0.7542
Epoch 49/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.1175 - accuracy: 0.8469
39/39 [==============================] - 2s 49ms/step - loss: 1.1175 - accuracy: 0.8469 - val_loss: 1.3889 - val_accuracy: 0.7604
Epoch 50/100
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39/39 [==============================] - 1s 37ms/step - loss: 1.0896 - accuracy: 0.8495
39/39 [==============================] - 2s 43ms/step - loss: 1.0896 - accuracy: 0.8495 - val_loss: 1.3804 - val_accuracy: 0.7625
Epoch 51/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.0962 - accuracy: 0.8510
39/39 [==============================] - 2s 48ms/step - loss: 1.0962 - accuracy: 0.8510 - val_loss: 1.4388 - val_accuracy: 0.7333
Epoch 52/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.1194 - accuracy: 0.8339
39/39 [==============================] - 2s 49ms/step - loss: 1.1194 - accuracy: 0.8339 - val_loss: 1.3961 - val_accuracy: 0.7792
Epoch 53/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.0942 - accuracy: 0.8391
39/39 [==============================] - 2s 44ms/step - loss: 1.0942 - accuracy: 0.8391 - val_loss: 1.4196 - val_accuracy: 0.7521
Epoch 54/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.0497 - accuracy: 0.8625
39/39 [==============================] - 2s 49ms/step - loss: 1.0497 - accuracy: 0.8625 - val_loss: 1.4607 - val_accuracy: 0.7937
Epoch 55/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.0560 - accuracy: 0.8661
39/39 [==============================] - 2s 44ms/step - loss: 1.0560 - accuracy: 0.8661 - val_loss: 1.4135 - val_accuracy: 0.7917
Epoch 56/100
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39/39 [==============================] - 2s 41ms/step - loss: 1.0484 - accuracy: 0.8604
39/39 [==============================] - 2s 49ms/step - loss: 1.0484 - accuracy: 0.8604 - val_loss: 1.4665 - val_accuracy: 0.7750
Epoch 57/100
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39/39 [==============================] - 1s 36ms/step - loss: 1.0535 - accuracy: 0.8604
39/39 [==============================] - 2s 45ms/step - loss: 1.0535 - accuracy: 0.8604 - val_loss: 1.4029 - val_accuracy: 0.7583
Epoch 58/100
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39/39 [==============================] - 2s 40ms/step - loss: 1.0223 - accuracy: 0.8589
39/39 [==============================] - 2s 49ms/step - loss: 1.0223 - accuracy: 0.8589 - val_loss: 1.4266 - val_accuracy: 0.7729
Epoch 59/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.9953 - accuracy: 0.8693
39/39 [==============================] - 2s 44ms/step - loss: 0.9953 - accuracy: 0.8693 - val_loss: 1.4173 - val_accuracy: 0.7812
Epoch 60/100
1/39 [..............................] - ETA: 0s - loss: 0.9652 - accuracy: 0.8400
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39/39 [==============================] - 2s 40ms/step - loss: 1.0114 - accuracy: 0.8682
39/39 [==============================] - 2s 49ms/step - loss: 1.0114 - accuracy: 0.8682 - val_loss: 1.4970 - val_accuracy: 0.7708
Epoch 61/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.9774 - accuracy: 0.8760
39/39 [==============================] - 2s 43ms/step - loss: 0.9774 - accuracy: 0.8760 - val_loss: 1.4325 - val_accuracy: 0.7583
Epoch 62/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.9871 - accuracy: 0.8734
39/39 [==============================] - 2s 51ms/step - loss: 0.9871 - accuracy: 0.8734 - val_loss: 1.4680 - val_accuracy: 0.7708
Epoch 63/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.9521 - accuracy: 0.8854
39/39 [==============================] - 2s 43ms/step - loss: 0.9521 - accuracy: 0.8854 - val_loss: 1.4232 - val_accuracy: 0.7625
Epoch 64/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.9656 - accuracy: 0.8740
39/39 [==============================] - 2s 49ms/step - loss: 0.9656 - accuracy: 0.8740 - val_loss: 1.4387 - val_accuracy: 0.7688
Epoch 65/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.9735 - accuracy: 0.8823
39/39 [==============================] - 2s 44ms/step - loss: 0.9735 - accuracy: 0.8823 - val_loss: 1.4134 - val_accuracy: 0.7667
Epoch 66/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.9402 - accuracy: 0.8750
39/39 [==============================] - 2s 46ms/step - loss: 0.9402 - accuracy: 0.8750 - val_loss: 1.4612 - val_accuracy: 0.7646
Epoch 67/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.9556 - accuracy: 0.8766
39/39 [==============================] - 2s 46ms/step - loss: 0.9556 - accuracy: 0.8766 - val_loss: 1.4638 - val_accuracy: 0.7312
Epoch 68/100
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39/39 [==============================] - 1s 38ms/step - loss: 0.9297 - accuracy: 0.8760
39/39 [==============================] - 2s 45ms/step - loss: 0.9297 - accuracy: 0.8760 - val_loss: 1.3763 - val_accuracy: 0.7708
Epoch 69/100
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39/39 [==============================] - 1s 38ms/step - loss: 0.9233 - accuracy: 0.8802
39/39 [==============================] - 2s 47ms/step - loss: 0.9233 - accuracy: 0.8802 - val_loss: 1.3756 - val_accuracy: 0.7792
Epoch 70/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.9167 - accuracy: 0.8813
39/39 [==============================] - 2s 43ms/step - loss: 0.9167 - accuracy: 0.8813 - val_loss: 1.3680 - val_accuracy: 0.7479
Epoch 71/100
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39/39 [==============================] - 2s 39ms/step - loss: 0.9243 - accuracy: 0.8755
39/39 [==============================] - 2s 48ms/step - loss: 0.9243 - accuracy: 0.8755 - val_loss: 1.3754 - val_accuracy: 0.7750
Epoch 72/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.8918 - accuracy: 0.8906
39/39 [==============================] - 2s 43ms/step - loss: 0.8918 - accuracy: 0.8906 - val_loss: 1.4000 - val_accuracy: 0.7812
Epoch 73/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.8726 - accuracy: 0.8958
39/39 [==============================] - 2s 49ms/step - loss: 0.8726 - accuracy: 0.8958 - val_loss: 1.4653 - val_accuracy: 0.7771
Epoch 74/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.8496 - accuracy: 0.8964
39/39 [==============================] - 2s 44ms/step - loss: 0.8496 - accuracy: 0.8964 - val_loss: 1.4708 - val_accuracy: 0.7771
Epoch 75/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.8483 - accuracy: 0.9000
39/39 [==============================] - 2s 47ms/step - loss: 0.8483 - accuracy: 0.9000 - val_loss: 1.4772 - val_accuracy: 0.7979
Epoch 76/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.8450 - accuracy: 0.8995
39/39 [==============================] - 2s 45ms/step - loss: 0.8450 - accuracy: 0.8995 - val_loss: 1.4168 - val_accuracy: 0.7917
Epoch 77/100
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39/39 [==============================] - 2s 39ms/step - loss: 0.8703 - accuracy: 0.8911
39/39 [==============================] - 2s 45ms/step - loss: 0.8703 - accuracy: 0.8911 - val_loss: 1.5344 - val_accuracy: 0.7875
Epoch 78/100
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39/39 [==============================] - 1s 38ms/step - loss: 0.8738 - accuracy: 0.8906
39/39 [==============================] - 2s 46ms/step - loss: 0.8738 - accuracy: 0.8906 - val_loss: 1.4429 - val_accuracy: 0.7854
Epoch 79/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.8732 - accuracy: 0.8911
39/39 [==============================] - 2s 43ms/step - loss: 0.8732 - accuracy: 0.8911 - val_loss: 1.3851 - val_accuracy: 0.7625
Epoch 80/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.8375 - accuracy: 0.8953
39/39 [==============================] - 2s 48ms/step - loss: 0.8375 - accuracy: 0.8953 - val_loss: 1.3983 - val_accuracy: 0.7667
Epoch 81/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.8487 - accuracy: 0.8849
39/39 [==============================] - 2s 42ms/step - loss: 0.8487 - accuracy: 0.8849 - val_loss: 1.5364 - val_accuracy: 0.8208
Epoch 82/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.8272 - accuracy: 0.8953
39/39 [==============================] - 2s 49ms/step - loss: 0.8272 - accuracy: 0.8953 - val_loss: 1.4269 - val_accuracy: 0.7625
Epoch 83/100
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39/39 [==============================] - 1s 34ms/step - loss: 0.8170 - accuracy: 0.8969
39/39 [==============================] - 2s 43ms/step - loss: 0.8170 - accuracy: 0.8969 - val_loss: 1.4062 - val_accuracy: 0.7812
Epoch 84/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.8122 - accuracy: 0.8979
39/39 [==============================] - 2s 47ms/step - loss: 0.8122 - accuracy: 0.8979 - val_loss: 1.3602 - val_accuracy: 0.7771
Epoch 85/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.8168 - accuracy: 0.8901
39/39 [==============================] - 2s 45ms/step - loss: 0.8168 - accuracy: 0.8901 - val_loss: 1.4381 - val_accuracy: 0.7854
Epoch 86/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.7839 - accuracy: 0.9026
39/39 [==============================] - 2s 44ms/step - loss: 0.7839 - accuracy: 0.9026 - val_loss: 1.4611 - val_accuracy: 0.8021
Epoch 87/100
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39/39 [==============================] - 2s 39ms/step - loss: 0.7740 - accuracy: 0.9219
39/39 [==============================] - 2s 48ms/step - loss: 0.7740 - accuracy: 0.9219 - val_loss: 1.3854 - val_accuracy: 0.7750
Epoch 88/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.7590 - accuracy: 0.9177
39/39 [==============================] - 2s 43ms/step - loss: 0.7590 - accuracy: 0.9177 - val_loss: 1.4130 - val_accuracy: 0.7792
Epoch 89/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.7534 - accuracy: 0.9146
39/39 [==============================] - 2s 48ms/step - loss: 0.7534 - accuracy: 0.9146 - val_loss: 1.4488 - val_accuracy: 0.7917
Epoch 90/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.7390 - accuracy: 0.9083
39/39 [==============================] - 2s 44ms/step - loss: 0.7390 - accuracy: 0.9083 - val_loss: 1.4028 - val_accuracy: 0.7896
Epoch 91/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.7485 - accuracy: 0.9089
39/39 [==============================] - 2s 43ms/step - loss: 0.7485 - accuracy: 0.9089 - val_loss: 1.4807 - val_accuracy: 0.8000
Epoch 92/100
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39/39 [==============================] - 2s 39ms/step - loss: 0.7523 - accuracy: 0.9057
39/39 [==============================] - 2s 47ms/step - loss: 0.7523 - accuracy: 0.9057 - val_loss: 1.3704 - val_accuracy: 0.8042
Epoch 93/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.7327 - accuracy: 0.9094
39/39 [==============================] - 2s 44ms/step - loss: 0.7327 - accuracy: 0.9094 - val_loss: 1.3603 - val_accuracy: 0.7896
Epoch 94/100
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39/39 [==============================] - 2s 39ms/step - loss: 0.7348 - accuracy: 0.9161
39/39 [==============================] - 2s 46ms/step - loss: 0.7348 - accuracy: 0.9161 - val_loss: 1.4322 - val_accuracy: 0.8021
Epoch 95/100
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39/39 [==============================] - 1s 37ms/step - loss: 0.7353 - accuracy: 0.9125
39/39 [==============================] - 2s 45ms/step - loss: 0.7353 - accuracy: 0.9125 - val_loss: 1.3779 - val_accuracy: 0.8000
Epoch 96/100
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39/39 [==============================] - 1s 36ms/step - loss: 0.7391 - accuracy: 0.9109
39/39 [==============================] - 2s 43ms/step - loss: 0.7391 - accuracy: 0.9109 - val_loss: 1.4209 - val_accuracy: 0.7979
Epoch 97/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.7098 - accuracy: 0.9146
39/39 [==============================] - 2s 48ms/step - loss: 0.7098 - accuracy: 0.9146 - val_loss: 1.3965 - val_accuracy: 0.7833
Epoch 98/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.7227 - accuracy: 0.8995
39/39 [==============================] - 2s 43ms/step - loss: 0.7227 - accuracy: 0.8995 - val_loss: 1.3536 - val_accuracy: 0.7708
Epoch 99/100
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39/39 [==============================] - 2s 40ms/step - loss: 0.7077 - accuracy: 0.9073
39/39 [==============================] - 2s 46ms/step - loss: 0.7077 - accuracy: 0.9073 - val_loss: 1.3884 - val_accuracy: 0.7896
Epoch 100/100
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39/39 [==============================] - 1s 35ms/step - loss: 0.6793 - accuracy: 0.9198
39/39 [==============================] - 2s 44ms/step - loss: 0.6793 - accuracy: 0.9198 - val_loss: 1.4331 - val_accuracy: 0.8250
Error in WeightedROC(prob_pred, label_test, weight_test) :
length(label) == length(guess) is not TRUE
Visualize cross-validation results.
res_cv_dnn <- as.data.frame(res_cv_dnn)
if(run.cv_dnn){
p1 <- res_cv_dnn %>%
ggplot(aes(x = lrs, y = mean_error,
ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
geom_crossbar() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2 <- res_cv_dnn %>%
ggplot(aes(x = lrs, y = mean_AUC,
ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
geom_crossbar() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p1)
print(p2)
}
# par_lr_best <- as.numeric(res_cv_dnn[which.min(res_cv_dnn$mean_error), 1])
par_lr_best = 0.0002
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 1, 0)
weight_test <- rep(NA, length(label_test))
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test, par_lr_best))
} else {
tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL, par_lr_best))
}
save_model_hdf5(fit_train, "../output/fit_train_dnn.h5")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
fit_train <- load_model_hdf5("../output/fit_train_dnn.h5")
tm_test <- system.time({label_pred <- test_dnn(fit_train, feature_test, type = "predict_classes");})
prob_pred <- test_dnn(fit_train, feature_test, type = "predict_proba")
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "DNN with lr =", par_lr_best, "is", accu*100, "%.\n")
cat("The AUC of model:", "DNN", "is", auc, ".\n")
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n")
cat("Time for testing model=", tm_test[3], "s \n")
###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.